Preoperative prolapse phenotype is predictive of surgical outcome with minimally invasive sacrocolpopexy.

BACKGROUND: The gold-standard treatment for advanced pelvic organ prolapse is sacrocolpopexy. However, the preoperative features of prolapse that predict optimal outcomes are unknown. OBJECTIVE: This study aimed to develop a clinical prediction model that uses preoperative scores on the Pelvic Organ Prolapse Quantification examination to predict outcomes after minimally invasive sacrocolpopexy for stages 2, 3, and 4 uterovaginal prolapse and vaginal vault prolapse. STUDY DESIGN: A 2-institution database of pre- and postoperative variables from 881 cases of minimally invasive sacrocolpopexy was analyzed. Data from patients were analyzed in the following 4 groups: stage 2 uterovaginal prolapse, stage 3 to 4 uterovaginal prolapse, stage 2 vaginal vault prolapse, and stage 3 to 4 vaginal vault prolapse. Unsupervised machine learning was used to identify clusters and investigate associations between clusters and outcome. The k-means clustering analysis was performed with preoperative Pelvic Organ Prolapse Quantification points and stratified by previous hysterectomy status. The "optimal" surgical outcome was defined as postoperative Pelvic Organ Prolapse Quantification stage <2. Demographic variables were compared by cluster with Student t and chi-square tests. Odds ratios were calculated to determine whether clusters could predict the outcome. Age at surgery, body mass index, and previous prolapse surgery were used for adjusted odds ratios. RESULTS: Five statistically distinct prolapse clusters (phenotypes C, A, A>P, P, and P>A) were found. These phenotypes reflected the predominant region of prolapse (apical, anterior, or posterior) and whether support was preserved in the nonpredominant region. Phenotype A (anterior compartment prolapse predominant, posterior support preserved) was found in all 4 groups of patients and was considered the reference in the analysis. In 111 patients with stage 2 uterovaginal prolapse, phenotypes A and A>P (greater anterior prolapse than posterior prolapse) were found, and patients with phenotype A were more likely than those with phenotype A>P to have an optimal surgical outcome. In 401 patients with stage 3 to 4 uterovaginal prolapse, phenotypes C (apical compartment predominant, prolapse in all compartments), A, and A>P were found, and patients with phenotype A>P were more likely than those with phenotype A to have ideal surgical outcome. In 72 patients with stage 2 vaginal vault prolapse, phenotypes A, A>P, and P (posterior compartment predominant, anterior support preserved) were found, and those with phenotype A>P were less likely to have an ideal outcome than patients with phenotype A. In 297 patients with stage 3 to 4 vaginal vault prolapse, phenotypes C, A, and P>A (prolapse greater in posterior than in anterior compartment) were found, but there were no significant differences in rate of ideal outcome between phenotypes. CONCLUSION: Five anatomic phenotypes based on preoperative Pelvic Organ Prolapse Quantification scores were present in patients with stages 2 and 3 to 4 uterovaginal prolapse and vaginal vault prolapse. These phenotypes are predictive of surgical outcome after minimally invasive sacrocolpopexy. Further work needs to confirm the presence and predictive nature of these phenotypes. In addition, whether the phenotypes represent a progression of prolapse or discrete prolapse presentations resulting from different anatomic and life course risk profiles is unknown. These phenotypes may be useful in surgical counseling and planning.

Shear wave speeds in a nearly incompressible fibrous material with two unequal fiber families.

The mechanical properties of soft biological tissues can be characterized non-invasively by magnetic resonance elastography (MRE). In MRE, shear wave fields are induced by vibration, imaged by magnetic resonance imaging, and inverted to estimate tissue properties in terms of the parameters of an underlying material model. Most MRE studies assume an isotropic material model; however, biological tissue is often anisotropic with a fibrous structure, and some tissues contain two or more families of fibers-each with different orientations and properties. Motivated by the prospect of using MRE to characterize such tissues, this paper describes the propagation of shear waves in soft fibrous material with two unequal fiber families. Shear wave speeds are expressed in terms of material parameters, and the effect of each parameter on the shear wave speeds is investigated. Analytical expressions of wave speeds are confirmed by finite element simulations of shear wave transmission with various polarization directions. This study supports the feasibility of estimating parameters of soft fibrous tissues with two unequal fiber families in vivo from local shear wave speeds and advances the prospects for the mechanical characterization of such biological tissues by MRE.

Theoretical relationships between axoneme distortion and internal forces and torques in ciliary beating.

The axoneme is an intricate nanomachine responsible for generating the propulsive oscillations of cilia and flagella in an astonishing variety of organisms. New imaging techniques based on cryoelectron-tomography (cryo-ET) and subtomogram averaging have revealed the detailed structures of the axoneme and its components with sub-nm resolution, but the mechanical function of each component and how the assembly generates oscillations remains stubbornly unclear. Most explanations of oscillatory behavior rely on the dynamic regulation of dynein by some signal, but this may not be necessary if the system of dynein-driven slender filaments is dynamically unstable. Understanding the possibility of instability-driven oscillations requires a multifilament model of the axoneme that accounts for distortions of the axoneme as it bends. Active bending requires forces and bending moments that will tend to change the spacing and alignment of doublets. We hypothesize that components of the axoneme resist and respond to these loads in ways that are critical to beating. Specifically, we propose (i) that radial spokes provide torsional stiffness by resisting misalignment (as well as spacing) between the central pair and outer doublets, and (ii) that the kinematics of dynein arms affect the relationships between active forces and bending moments on deforming doublets. These proposed relationships enhance the ability of theoretical, multifilament models of axonemal beating to generate propulsive oscillatory waveforms via dynamic mechanical instability.

Structural tuning of anisotropic mechanical properties in 3D-Printed hydrogel lattices.

We investigated the ability to tune the anisotropic mechanical properties of 3D-printed hydrogel lattices by modifying their geometry (lattice strut diameter, unit cell size, and unit cell scaling factor). Many soft tissues are anisotropic and the ability to mimic natural anisotropy would be valuable for developing tissue-surrogate "phantoms" for elasticity imaging (shear wave elastography or magnetic resonance elastography). Vintile lattices were 3D-printed in polyethylene glycol di-acrylate (PEGDA) using digital light projection printing. Two mechanical benchtop tests, dynamic shear testing and unconfined compression, were used to measure the apparent shear storage moduli (G') and apparent Young's moduli (E) of lattice samples. Increasing the unit cell size from 1.25 mm to 2.00 mm reduced the Young's and shear moduli of the lattices by 91% and 85%, respectively. Decreasing the strut diameter from 300 µm to 200 µm reduced the apparent shear moduli of the lattices by 95%. Increasing the geometric scaling ratio of the lattice unit cells from 1.00 × to 2.00 × increased mechanical anisotropy in shear (by a factor of 3.1) and in compression (by a factor of 2.9). Both simulations and experiments show that the effects of unit cell size and strut diameter are consistent with power law relationships between volume fraction and apparent elastic moduli. In particular, experimental measurements of apparent Young's moduli agree well with predictions of the theoretical Gibson-Ashby model. Thus, the anisotropic mechanical properties of a lattice can be tuned by the unit cell size, the strut diameter, and scaling factors. This approach will be valuable in designing tissue-mimicking hydrogel lattice-based composite materials for elastography phantoms and tissue engineered scaffolds.

Post-mortem changes of anisotropic mechanical properties in the porcine brain assessed by MR elastography.

Knowledge of the mechanical properties of brain tissue in vivo is essential to understanding the mechanisms underlying traumatic brain injury (TBI) and to creating accurate computational models of TBI and neurosurgical simulation. Brain white matter, which is composed of aligned, myelinated, axonal fibers, is structurally anisotropic. White matter in vivo also exhibits mechanical anisotropy, as measured by magnetic resonance elastography (MRE), but measurements of anisotropy obtained by mechanical testing of white matter ex vivo have been inconsistent. The minipig has a gyrencephalic brain with similar white matter and gray matter proportions to humans and therefore provides a relevant model for human brain mechanics. In this study, we compare estimates of anisotropic mechanical properties of the minipig brain obtained by identical, non-invasive methods in the live (in vivo) and dead animals (in situ). To do so, we combine wave displacement fields from MRE and fiber directions derived from diffusion tensor imaging (DTI) with a finite element-based, transversely-isotropic nonlinear inversion (TI-NLI) algorithm. Maps of anisotropic mechanical properties in the minipig brain were generated for each animal alive and at specific times post-mortem. These maps show that white matter is stiffer, more dissipative, and more anisotropic than gray matter when the minipig is alive, but that these differences largely disappear post-mortem, with the exception of tensile anisotropy. Overall, brain tissue becomes stiffer, less dissipative, and less mechanically anisotropic post-mortem. These findings emphasize the importance of testing brain tissue properties in vivo. Statement of Significance: In this study, MRE and DTI in the minipig were combined to estimate, for the first time, anisotropic mechanical properties in the living brain and in the same brain after death. Significant differences were observed in the anisotropic behavior of brain tissue post-mortem. These results demonstrate the importance of measuring brain tissue properties in vivo as well as ex vivo, and provide new quantitative data for the development of computational models of brain biomechanics.

Mechanical Properties of White Matter Tracts in Aging Assessed via Anisotropic MR Elastography.

Magnetic resonance elastography (MRE) is a promising neuroimaging technique to probe tissue microstructure, which has revealed widespread softening with loss of structural integrity in the aging brain. Traditional MRE approaches assume mechanical isotropy. However, white matter is known to be anisotropic from aligned, myelinated axonal bundles, which can lead to uncertainty in mechanical property estimates in these areas when using isotropic MRE. Recent advances in anisotropic MRE now allow for estimation of shear and tensile anisotropy, along with substrate shear modulus, in white matter tracts. The objective of this study was to investigate age-related differences in anisotropic mechanical properties in human brain white matter tracts for the first time. Anisotropic mechanical properties in all tracts were found to be significantly lower in older adults compared to young adults, with average property differences ranging between 0.028-0.107 for shear anisotropy and between 0.139-0.347 for tensile anisotropy. Stiffness perpendicular to the axonal fiber direction was also significantly lower in older age, but only in certain tracts. When compared with fractional anisotropy measures from diffusion tensor imaging, we found that anisotropic MRE measures provided additional, complementary information in describing differences between the white matter integrity of young and older populations. Anisotropic MRE provides a new tool for studying white matter structural integrity in aging and neurodegeneration.

Effect of Human Head Shape on the Risk of Traumatic Brain Injury: A Gaussian Process Regression-based Machine Learning Approach.

INTRODUCTION: Computational head injury models are promising tools for understanding and predicting traumatic brain injuries. However, most available head injury models are "average" models that employ a single set of head geometry (e.g., 50th-percentile U.S. male) without considering variability in these parameters across the human population. A significant variability of head shapes exists in U.S. Army soldiers, evident from the Anthropometric Survey of U.S. Army Personnel (ANSUR II). The objective of this study is to elucidate the effects of head shape on the predicted risk of traumatic brain injury from computational head injury models. MATERIALS AND METHODS: Magnetic resonance imaging scans of 25 human subjects are collected. These images are registered to the standard MNI152 brain atlas, and the resulting transformation matrix components (called head shape parameters) are used to quantify head shapes of the subjects. A generative machine learning model is used to generate 25 additional head shape parameter datasets to augment our database. Head injury models are developed for these head shapes, and a rapid injurious head rotation event is simulated to obtain several brain injury predictor variables (BIPVs): Peak cumulative maximum principal strain (CMPS), average CMPS, and the volume fraction of brain exceeding an injurious CMPS threshold. A Gaussian process regression model is trained between head shape parameters and BIPVs, which is then used to study the relative sensitivity of the various BIPVs on individual head shape parameters. We distinguish head shape parameters into 2 types: Scaling components ${T_{xx}}$, ${T_{yy}}$, and ${T_{zz}}$ that capture the breadth, length, and height of the head, respectively, and shearing components (${T_{xy}},{T_{xz}},{T_{yx}},{T_{yz}},{T_{zx}}$, and ${T_{zy}}$) that capture the relative skewness of the head shape. RESULTS: An overall positive correlation is evident between scaling components and BIPVs. Notably, a very high, positive correlation is seen between the BIPVs and the head volume. As an example, a 57% increase in peak CMPS was noted between the smallest and the largest investigated head volume parameters. The variation in shearing components ${T_{xy}},{T_{xz}},{T_{yx}},{T_{yz}},{T_{zx}}$, and ${T_{zy}}$ on average does not cause notable changes in the BIPVs. From the Gaussian process regression model, all 3 BIPVs showed an increasing trend with each of the 3 scaling components, but the BIPVs are found to be most sensitive to the height dimension of the head. From the Sobol sensitivity analysis, the ${T_{zz}}$ scaling parameter contributes nearly 60% to the total variance in peak and average CMPS; ${T_{yy}}$ contributes approximately 20%, whereas ${T_{xx}}$ contributes less than 5%. The remaining contribution is from the 6 shearing components. Unlike peak and average CMPS, the VF-CMPS BIPV is associated with relatively evenly distributed Sobol indices across the 3 scaling parameters. Furthermore, the contribution of shearing components on the total variance in this case is negligible. CONCLUSIONS: Head shape has a considerable influence on the injury predictions of computational head injury models. Available "average" head injury models based on a 50th-percentile U.S. male are likely associated with considerable uncertainty. In general, larger head sizes correspond to greater BIPV magnitudes, which point to potentially a greater injury risk under rapid neck rotation for people with larger heads.

Effects of stress-dependent growth on evolution of sulcal direction and curvature in models of cortical folding.

The majority of human brain folding occurs during the third trimester of gestation. Although many studies have investigated the physical mechanisms of brain folding, a comprehensive understanding of this complex process has not yet been achieved. In mechanical terms, the "differential growth hypothesis" suggests that the formation of folds results from a difference in expansion rates between cortical and subcortical layers, which eventually leads to mechanical instability akin to buckling. It has also been observed that axons, a substantial component of subcortical tissue, can elongate or shrink under tensile or compressive stress, respectively. Previous work has proposed that this cell-scale behavior in aggregate can produce stress-dependent growth in the subcortical layers. The current study investigates the potential role of stress-dependent growth on cortical surface morphology, in particular the variations in folding direction and curvature over the course of development. Evolution of sulcal direction and mid-cortical surface curvature were calculated from finite element simulations of three-dimensional folding in four different initial geometries: (i) sphere; (ii) axisymmetric oblate spheroid; (iii) axisymmetric prolate spheroid; and (iv) triaxial spheroid. The results were compared to mid-cortical surface reconstructions from four preterm human infants, imaged and analyzed at four time points during the period of brain folding. Results indicate that models incorporating subcortical stress-dependent growth predict folding patterns that more closely resemble those in the developing human brain. Statement of Significance: Cortical folding is a critical process in human brain development. Aberrant folding is associated with disorders such as autism and schizophrenia, yet our understanding of the physical mechanism of folding remains limited. Ultimately mechanical forces must shape the brain. An important question is whether mechanical forces simply deform tissue elastically, or whether stresses in the tissue modulate growth. Evidence from this paper, consisting of quantitative comparisons between patterns of folding in the developing human brain and corresponding patterns in simulations, supports a key role for stress-dependent growth in cortical folding.